{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8b0e11f2-9ea4-48c2-b8d2-d0a4ba967827",
   "metadata": {},
   "source": [
    "# Gradio Day!\n",
    "\n",
    "Today we will build User Interfaces using the outrageously simple Gradio framework.\n",
    "\n",
    "Prepare for joy!\n",
    "\n",
    "Please note: your Gradio screens may appear in 'dark mode' or 'light mode' depending on your computer settings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c44c5494-950d-4d2f-8d4f-b87b57c5b330",
   "metadata": {},
   "outputs": [],
   "source": [
    "# imports\n",
    "\n",
    "import os\n",
    "import requests\n",
    "from bs4 import BeautifulSoup\n",
    "from typing import List\n",
    "from dotenv import load_dotenv\n",
    "from openai import OpenAI\n",
    "#import google.generativeai\n",
    "#import anthropic\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d1715421-cead-400b-99af-986388a97aff",
   "metadata": {},
   "outputs": [],
   "source": [
    "import gradio as gr # oh yeah!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "22586021-1795-4929-8079-63f5bb4edd4c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "API key looks good so far\n"
     ]
    }
   ],
   "source": [
    "# Connect to OpenAI, Anthropic and Google; comment out the Claude or Google lines if you're not using them\n",
    "\n",
    "# openai = OpenAI()\n",
    "\n",
    "# claude = anthropic.Anthropic()\n",
    "\n",
    "# google.generativeai.configure()\n",
    "\n",
    "load_dotenv(override=True)\n",
    "\n",
    "api_key = os.getenv('Open_Router_Key')\n",
    "if api_key and api_key.startswith('sk-or-v1') and len(api_key)>10:\n",
    "   print(\"API key looks good so far\")\n",
    "else:\n",
    "   print(\"There might be a problem with your API key? Please visit the troubleshooting notebook!\")\n",
    "  \n",
    "  \n",
    "openai = OpenAI(\n",
    "    api_key=api_key,\n",
    "    base_url=\"https://openrouter.ai/api/v1\"\n",
    ")\n",
    "\n",
    "MODEL_Gemini2FlashLite = 'google/gemini-2.0-flash-lite-preview-02-05:free'\n",
    "MODEL_Gemini2FlashThink = 'google/gemini-2.0-flash-thinking-exp:free'\n",
    "MODEL_Gemini2Pro ='google/gemini-2.0-pro-exp-02-05:free'\n",
    "MODEL_Meta_Llama33 ='meta-llama/llama-3.3-70b-instruct:free'\n",
    "MODEL_Deepseek_V3='deepseek/deepseek-chat:free'\n",
    "MODEL_Deepseek_R1='deepseek/deepseek-r1-distill-llama-70b:free'\n",
    "MODEL_Qwen_vlplus='qwen/qwen-vl-plus:free'\n",
    "MODEL_OpenAi_o3mini = 'openai/o3-mini'\n",
    "MODEL_OpenAi_4o = 'openai/gpt-4o-2024-11-20'\n",
    "MODEL_Claude_Haiku = 'anthropic/claude-3.5-haiku-20241022'\n",
    "\n",
    "\n",
    "Default_Model = MODEL_Deepseek_V3\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b16e6021-6dc4-4397-985a-6679d6c8ffd5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# A generic system message - no more snarky adversarial AIs!\n",
    "\n",
    "system_message = \"You are a helpful assistant\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "02ef9b69-ef31-427d-86d0-b8c799e1c1b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's wrap a call to GPT-4o-mini in a simple function\n",
    "\n",
    "def message_gpt(prompt):\n",
    "    messages = [\n",
    "        {\"role\": \"system\", \"content\": system_message},\n",
    "        {\"role\": \"user\", \"content\": prompt}\n",
    "      ]\n",
    "    completion = openai.chat.completions.create(\n",
    "        model=Default_Model,\n",
    "        messages=messages,\n",
    "    )\n",
    "    return completion.choices[0].message.content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "aef7d314-2b13-436b-b02d-8de3b72b193f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Today is October 26, 2023.\\n'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This can reveal the \"training cut off\", or the most recent date in the training data\n",
    "\n",
    "message_gpt(\"What is today's date?\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f94013d1-4f27-4329-97e8-8c58db93636a",
   "metadata": {},
   "source": [
    "## User Interface time!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bc664b7a-c01d-4fea-a1de-ae22cdd5141a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# here's a simple function\n",
    "\n",
    "def shout(text):\n",
    "    print(f\"Shout has been called with input {text}\")\n",
    "    return text.upper()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "083ea451-d3a0-4d13-b599-93ed49b975e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shout has been called with input hello\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'HELLO'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shout(\"hello\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "08f1f15a-122e-4502-b112-6ee2817dda32",
   "metadata": {},
   "outputs": [],
   "source": [
    "# The simplicty of gradio. This might appear in \"light mode\" - I'll show you how to make this in dark mode later.\n",
    "\n",
    "gr.Interface(fn=shout, inputs=\"textbox\", outputs=\"textbox\").launch()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c9a359a4-685c-4c99-891c-bb4d1cb7f426",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adding share=True means that it can be accessed publically\n",
    "# A more permanent hosting is available using a platform called Spaces from HuggingFace, which we will touch on next week\n",
    "# NOTE: Some Anti-virus software and Corporate Firewalls might not like you using share=True. If you're at work on on a work network, I suggest skip this test.\n",
    "\n",
    "gr.Interface(fn=shout, inputs=\"textbox\", outputs=\"textbox\", flagging_mode=\"never\").launch(share=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cd87533a-ff3a-4188-8998-5bedd5ba2da3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adding inbrowser=True opens up a new browser window automatically\n",
    "\n",
    "gr.Interface(fn=shout, inputs=\"textbox\", outputs=\"textbox\", flagging_mode=\"never\").launch(inbrowser=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b42ec007-0314-48bf-84a4-a65943649215",
   "metadata": {},
   "source": [
    "## Forcing dark mode\n",
    "\n",
    "Gradio appears in light mode or dark mode depending on the settings of the browser and computer. There is a way to force gradio to appear in dark mode, but Gradio recommends against this as it should be a user preference (particularly for accessibility reasons). But if you wish to force dark mode for your screens, below is how to do it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e8129afa-532b-4b15-b93c-aa9cca23a546",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define this variable and then pass js=force_dark_mode when creating the Interface\n",
    "\n",
    "force_dark_mode = \"\"\"\n",
    "function refresh() {\n",
    "    const url = new URL(window.location);\n",
    "    if (url.searchParams.get('__theme') !== 'dark') {\n",
    "        url.searchParams.set('__theme', 'dark');\n",
    "        window.location.href = url.href;\n",
    "    }\n",
    "}\n",
    "\"\"\"\n",
    "gr.Interface(fn=shout, inputs=\"textbox\", outputs=\"textbox\", flagging_mode=\"never\", js=force_dark_mode).launch()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3cc67b26-dd5f-406d-88f6-2306ee2950c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Inputs and Outputs\n",
    "\n",
    "view = gr.Interface(\n",
    "    fn=shout,\n",
    "    inputs=[gr.Textbox(label=\"Your message:\", lines=6)],\n",
    "    outputs=[gr.Textbox(label=\"Response:\", lines=8)],\n",
    "    flagging_mode=\"never\"\n",
    ")\n",
    "view.launch()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f235288e-63a2-4341-935b-1441f9be969b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# And now - changing the function from \"shout\" to \"message_gpt\"\n",
    "\n",
    "view = gr.Interface(\n",
    "    fn=message_gpt,\n",
    "    inputs=[gr.Textbox(label=\"Your message:\", lines=6)],\n",
    "    outputs=[gr.Textbox(label=\"Response:\", lines=8)],\n",
    "    flagging_mode=\"never\"\n",
    ")\n",
    "view.launch()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "af9a3262-e626-4e4b-80b0-aca152405e63",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's use Markdown\n",
    "# Are you wondering why it makes any difference to set system_message when it's not referred to in the code below it?\n",
    "# I'm taking advantage of system_message being a global variable, used back in the message_gpt function (go take a look)\n",
    "# Not a great software engineering practice, but quite sommon during Jupyter Lab R&D!\n",
    "\n",
    "system_message = \"You are a helpful assistant that responds in markdown\"\n",
    "\n",
    "view = gr.Interface(\n",
    "    fn=message_gpt,\n",
    "    inputs=[gr.Textbox(label=\"Your message:\")],\n",
    "    outputs=[gr.Markdown(label=\"Response:\")],\n",
    "    flagging_mode=\"never\"\n",
    ")\n",
    "view.launch()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "88c04ebf-0671-4fea-95c9-bc1565d4bb4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's create a call that streams back results\n",
    "# If you'd like a refresher on Generators (the \"yield\" keyword),\n",
    "# Please take a look at the Intermediate Python notebook in week1 folder.\n",
    "\n",
    "def stream_gpt(prompt):\n",
    "    messages = [\n",
    "        {\"role\": \"system\", \"content\": system_message},\n",
    "        {\"role\": \"user\", \"content\": prompt}\n",
    "      ]\n",
    "    stream = openai.chat.completions.create(\n",
    "        model=Default_Model,\n",
    "        messages=messages,\n",
    "        stream=True\n",
    "    )\n",
    "    result = \"\"\n",
    "    for chunk in stream:\n",
    "        result += chunk.choices[0].delta.content or \"\"\n",
    "        yield result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0bb1f789-ff11-4cba-ac67-11b815e29d09",
   "metadata": {},
   "outputs": [],
   "source": [
    "view = gr.Interface(\n",
    "    fn=stream_gpt,\n",
    "    inputs=[gr.Textbox(label=\"Your message:\")],\n",
    "    outputs=[gr.Markdown(label=\"Response:\")],\n",
    "    flagging_mode=\"never\"\n",
    ")\n",
    "view.launch()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bbc8e930-ba2a-4194-8f7c-044659150626",
   "metadata": {},
   "outputs": [],
   "source": [
    "# def stream_claude(prompt):\n",
    "#     result = claude.messages.stream(\n",
    "#         model=\"claude-3-haiku-20240307\",\n",
    "#         max_tokens=1000,\n",
    "#         temperature=0.7,\n",
    "#         system=system_message,\n",
    "#         messages=[\n",
    "#             {\"role\": \"user\", \"content\": prompt},\n",
    "#         ],\n",
    "#     )\n",
    "#     response = \"\"\n",
    "#     with result as stream:\n",
    "#         for text in stream.text_stream:\n",
    "#             response += text or \"\"\n",
    "#             yield response"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a0066ffd-196e-4eaf-ad1e-d492958b62af",
   "metadata": {},
   "outputs": [],
   "source": [
    "Default_Model=MODEL_Claude_Haiku\n",
    "view = gr.Interface(\n",
    "    fn=stream_gpt,\n",
    "    inputs=[gr.Textbox(label=\"Your message:\")],\n",
    "    outputs=[gr.Markdown(label=\"Response:\")],\n",
    "    flagging_mode=\"never\"\n",
    ")\n",
    "view.launch()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc5a70b9-2afe-4a7c-9bed-2429229e021b",
   "metadata": {},
   "source": [
    "## Minor improvement\n",
    "\n",
    "I've made a small improvement to this code.\n",
    "\n",
    "Previously, it had these lines:\n",
    "\n",
    "```\n",
    "for chunk in result:\n",
    "  yield chunk\n",
    "```\n",
    "\n",
    "There's actually a more elegant way to achieve this (which Python people might call more 'Pythonic'):\n",
    "\n",
    "`yield from result`\n",
    "\n",
    "I cover this in more detail in the Intermediate Python notebook in the week1 folder - take a look if you'd like more."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0087623a-4e31-470b-b2e6-d8d16fc7bcf5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def stream_model(prompt, model):\n",
    "    if model==\"GPT\":\n",
    "        Default_Model=MODEL_Gemini2FlashThink\n",
    "        result = stream_gpt(prompt)\n",
    "    elif model==\"Claude\":\n",
    "        Default_Model=MODEL_Claude_Haiku\n",
    "        result = stream_gpt(prompt)\n",
    "    else:\n",
    "        raise ValueError(\"Unknown model\")\n",
    "    yield from result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8d8ce810-997c-4b6a-bc4f-1fc847ac8855",
   "metadata": {},
   "outputs": [],
   "source": [
    "view = gr.Interface(\n",
    "    fn=stream_model,\n",
    "    inputs=[gr.Textbox(label=\"Your message:\"), gr.Dropdown([\"GPT\", \"Claude\"], label=\"Select model\", value=\"GPT\")],\n",
    "    outputs=[gr.Markdown(label=\"Response:\")],\n",
    "    flagging_mode=\"never\"\n",
    ")\n",
    "view.launch()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d933865b-654c-4b92-aa45-cf389f1eda3d",
   "metadata": {},
   "source": [
    "# Building a company brochure generator\n",
    "\n",
    "Now you know how - it's simple!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92d7c49b-2e0e-45b3-92ce-93ca9f962ef4",
   "metadata": {},
   "source": [
    "<table style=\"margin: 0; text-align: left;\">\n",
    "    <tr>\n",
    "        <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n",
    "            <img src=\"../../important.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n",
    "        </td>\n",
    "        <td>\n",
    "            <h2 style=\"color:#900;\">Before you read the next few cells</h2>\n",
    "            <span style=\"color:#900;\">\n",
    "                Try to do this yourself - go back to the company brochure in week1, day5 and add a Gradio UI to the end. Then come and look at the solution.\n",
    "            </span>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1626eb2e-eee8-4183-bda5-1591b58ae3cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# A class to represent a Webpage\n",
    "\n",
    "class Website:\n",
    "    url: str\n",
    "    title: str\n",
    "    text: str\n",
    "\n",
    "    def __init__(self, url):\n",
    "        self.url = url\n",
    "        response = requests.get(url)\n",
    "        self.body = response.content\n",
    "        soup = BeautifulSoup(self.body, 'html.parser')\n",
    "        self.title = soup.title.string if soup.title else \"No title found\"\n",
    "        for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n",
    "            irrelevant.decompose()\n",
    "        self.text = soup.body.get_text(separator=\"\\n\", strip=True)\n",
    "\n",
    "    def get_contents(self):\n",
    "        return f\"Webpage Title:\\n{self.title}\\nWebpage Contents:\\n{self.text}\\n\\n\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c701ec17-ecd5-4000-9f68-34634c8ed49d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# With massive thanks to Bill G. who noticed that a prior version of this had a bug! Now fixed.\n",
    "\n",
    "system_message = \"You are an assistant that analyzes the contents of a company website landing page \\\n",
    "and creates a short brochure about the company for prospective customers, investors and recruits. Respond in markdown.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5def90e0-4343-4f58-9d4a-0e36e445efa4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def stream_brochure(company_name, url, model):\n",
    "    prompt = f\"Please generate a company brochure for {company_name}. Here is their landing page:\\n\"\n",
    "    prompt += Website(url).get_contents()\n",
    "    if model==\"GPT\":\n",
    "        result = stream_gpt(prompt)\n",
    "    elif model==\"Claude\":\n",
    "        result = stream_claude(prompt)\n",
    "    else:\n",
    "        raise ValueError(\"Unknown model\")\n",
    "    yield from result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "66399365-5d67-4984-9d47-93ed26c0bd3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "view = gr.Interface(\n",
    "    fn=stream_brochure,\n",
    "    inputs=[\n",
    "        gr.Textbox(label=\"Company name:\"),\n",
    "        gr.Textbox(label=\"Landing page URL including http:// or https://\"),\n",
    "        gr.Dropdown([\"GPT\", \"Claude\"], label=\"Select model\")],\n",
    "    outputs=[gr.Markdown(label=\"Brochure:\")],\n",
    "    flagging_mode=\"never\"\n",
    ")\n",
    "view.launch()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ede97ca3-a0f8-4f6e-be17-d1de7fef9cc0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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